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Evidence record 12697 · automatically gathered

Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction

Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalised cancer care. This research investigates whether federated learning can support the development of robust models for predicting tumour progression in breast cancer patients while addressing four critical deployment pillars: transparency, scalability, security, and fairness. This study evaluates a federated learning fram

Record details

Published: 21 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Privacy · Healthcare · Transparency
Retrieved: 23 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Privacy

Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification

arXiv cs.CR (AI security) · 23 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability

arXiv · 3 August 2026

Shared topic: Bias & fairness · Shared topic: Privacy

FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings

arXiv · 5 May 2026

Shared topic: Bias & fairness · Shared topic: Healthcare

Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers

arXiv · 16 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Auditing Fairness-Privacy Trade-offs: Subpopulation-Level Effects of Fairness-Enhancing Algorithms

arXiv · 16 July 2026

Shared topic: Bias & fairness · Shared topic: Healthcare

Fairness Interventions in Classification: A Study on AI Explainability

arXiv cs.CY · 28 July 2026

How to cite this record

ethics.ai (21 July 2026), “Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction,” evidence record 12697, https://ethics.ai/record/12697 (originally published by arXiv).

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